J
Joseph P. Noonan
Researcher at Tufts University
Publications - 59
Citations - 2435
Joseph P. Noonan is an academic researcher from Tufts University. The author has contributed to research in topics: Encryption & Non-local means. The author has an hindex of 15, co-authored 59 publications receiving 1953 citations. Previous affiliations of Joseph P. Noonan include Phillips Laboratory & University of Texas at Austin.
Papers
More filters
NPCR and UACI Randomness Tests for Image Encryption
TL;DR: The question of whether a given NPCR/UACI score is sufficiently high such that it is not discernible from ideally encrypted images is answered by comparing actual NPCR and UACI scores with corresponding critical values.
Journal ArticleDOI
Local Shannon entropy measure with statistical tests for image randomness
TL;DR: The proposed local Shannon entropy measure overcomes several weaknesses of the conventional global Shannon entropyMeasure, including unfair randomness comparisons between images of different sizes, failure to discern image randomness before and after image shuffling, and possible inaccurate scores for synthesized images.
Journal ArticleDOI
Image encryption using the two-dimensional logistic chaotic map
TL;DR: The two-dimensional logistic map with complicated basin structures and attractors are first used for image encryption and the proposed method adopts the classic framework of the permutation-substitution network in cryptography to ensure both confusion and diffusion properties for a secure cipher.
Journal ArticleDOI
Design of image cipher using latin squares
TL;DR: A symmetric-key Latin square image cipher (LSIC) for grayscale and color image encryption is introduced that has many desired properties of a secure cipher, shows robustness against different attack models, and outperforms state of the art suggested by many peer algorithms.
Journal ArticleDOI
James–Stein Type Center Pixel Weights for Non-Local Means Image Denoising
TL;DR: The experimental results showed that compared to existing CPW solutions, the LJSCPW is more robust and effective under various noise levels and attains higher means with smaller variances in terms of the peak signal and noise ratio (PSNR) and structural similarity (SSIM).